Intelligent measurement of fundamental frequency of complex periodic signals based on maximum periodicity

The existing zero-crossing comparison and peak detection cannot intelligently identify a real period, thus lack the ability to intelligently measure complex signals. This paper presents a corrected equal-precision frequency measurement method and establishes a fundamental frequency measurement formula of complex periodic signals. An intelligent system is designed using digital sampling hardware and maximum periodicity software algorithm. The periodicity defined by Manhattan distance in relative error weighting form, describing how closely the measured signal resembles a periodic signal. The variable sliding window is used to supervise the periodicity. According to the length of the sliding window when the maximum periodicity first appeared, intelligent fundamental frequency measurement and intelligent classification are realized. The test data show the system can realize the high-precision, wide-range automatic and intelligent measurement under digital sampling conditions. When the SNR is higher than 13.98 dB, the measurement relative deviation of noisy complex periodic signal is less than 0.5%.